Selecting an AI Marketing Agency: RFP Template, Scorecard, and Red Flags

To select an AI marketing agency, define the business metric you need to move, shortlist three to five agencies that work in your channels, send every one of them the same RFP and data brief, score their proposals and demos on a weighted scorecard, and award a fixed-fee pilot with pass or fail criteria before you sign a long retainer. This guide gives you the RFP template, the scorecard, the interview questions and the red flags.

Still deciding whether to bring in outside help at all? Read our comparison of in-house vs agency vs managed AI content automation first.

The fast path to AI marketing agency selection

• Clarify outcomes before tools. Define the business problems and the measurable results you expect.
• Shortlist 3 to 5 agencies that match your industry, channels, and data complexity.
• Issue a tight RFP with requirements and scoring criteria upfront.
• Run structured demos against your use cases with the same data brief.
• Score independently, debrief as a team, and document the decision.

Match the agency type to the problem

Decide which problem tops your list before you write the RFP. It changes who belongs on the shortlist and which scorecard criteria get the most weight.

• Full-funnel growth: pipeline, CAC and retention all need work. See what an AI growth marketing agency does.
• Social and community: attention is the bottleneck. Test how the agency spots trends and handles replies at volume, as covered in AI social media agencies and community management.
• Organic search: weight technical depth and content QA, using the checks in evaluating an AI SEO agency.
• Brand and identity: if positioning is unclear, performance work underdelivers. Start with an AI branding agency.

Many brands need two or three of these at once. Work With AI's AI marketing agency services cover strategy, creative, paid, social, content and email under one team, with media buying handled by our AI advertising team.

What to define before you invite agencies

1. Business objectives: revenue, pipeline, CAC, LTV, retention, media efficiency, creative throughput.
2. Primary use cases: predictive segmentation, media mix modeling, lead scoring, content generation, SEO at scale, product recommendations, lifecycle automation.
3. Data and tools: CRMs, CDPs, analytics, MAPs, ad platforms, CMS, data warehouses, model hosting, experimentation stack.
4. Constraints: compliance, security, brand guardrails, geography, languages, resource availability.
5. Governance: who approves, who signs, reporting cadence, acceptable use policy for generative AI.

Copy-ready RFP template for AI marketing

Use the outline below as your RFP for AI marketing. Replace bracketed prompts with your details. Share the same brief with all vendors to keep the process fair.

Title: Request for Proposal: AI Marketing Services for [Company Name]

1. Background and Objectives
- About us: [Industry, markets, audience]
- Current state: [Channels, stack, team size]
- Objectives: [Revenue, pipeline, CAC, retention, efficiency]
- Success metrics: [Primary KPI, secondary KPIs, target lift or timeframe]

2. Scope of Services
- Use cases: [e.g., predictive lead scoring, content generation, MMM]
- Channels: [Paid search, paid social, SEO, email, web, retail media]
- Deliverables: [Models, playbooks, content assets, experiments]
- Collaboration model: [Pilot, retainer, embedded team, training]

3. Data and Technology Environment
- Data sources and access: [CRM, CDP, DWH, analytics]
- Data quality notes: [Gaps, latency, PII handling]
- Tooling constraints: [Approved vendors, on-prem vs cloud]
- Integration needs: [APIs, connectors, ETL, tag management]

4. Approach and Methodology
- Discovery and hypothesis process
- Model selection and evaluation
- Human-in-the-loop QA and compliance guardrails
- Experiment design and measurement standards

5. Team and Experience
- Roles and seniority mix
- Relevant case studies with metrics and methods
- Certifications and partnerships

6. Security, Compliance, and Ethics
- Data protection, encryption, residency
- DPA readiness and third-party tool vetting
- Content safety, bias mitigation, model governance

7. Work Plan and Timeline
- Milestones: [Kickoff, discovery, pilot, go-live]
- Dependencies and stakeholder time required

8. Pricing and Commercials
- Rate card and blended rates
- Fixed-fee pilot proposal with success criteria
- Licensing or usage-based costs
- Assumptions, exclusions, and change control

9. SLAs, Reporting, and Governance
- SLAs: [Turnaround, uptime if applicable]
- Reporting cadence and dashboards
- Executive reviews and escalation path

10. References and Validations
- 2-3 relevant client references
- Example artifacts: dashboards, prompts, templates

11. Evaluation Criteria and Scoring
- We will use the attached weighted scorecard (see below)

12. Submission Instructions
- Timeline: [Questions due, proposals due, demo dates]
- Format: [Page limit, file format]
- Contacts: [Primary contact, procurement]

Weighted evaluation scorecard

Score each agency from 1 to 5 per criterion, multiply by the weight, then sum for the total score. Keep individual scores private until the group debrief to avoid anchoring.

Criterion	Description	Weight	Score 1-5	Weighted
Impact on business goals	Clarity on how the plan drives revenue, CAC, LTV, or efficiency	15%
AI capabilities and methodology	Modeling rigor, prompt engineering, evaluation, and governance	15%
Data and integration readiness	Plan to ingest, clean, and connect to your stack	10%
Channel and domain expertise	Proof of performance in your channels and industry	8%
Measurement and experimentation	Lift measurement, MMM, incrementality, test design	8%
Team quality and seniority	Hands-on experts, not only advisors	12%
Security and compliance	Policies, tooling approvals, and control of third parties	7%
Case studies and proof	Baseline, counterfactuals, and clear methods	8%
Cultural and process fit	Ways of working, transparency, and communication	5%
Price and value	ROI potential, transparency, and risk sharing	12%
Total		100%

Adjust the weights to your problem (a regulated business might double the security weight), and lock them before proposals arrive so nobody tunes them toward a favourite.

Spreadsheet-ready scorecard template

Criteria,Weight,Score (1-5),Weighted Score,Notes
Impact on business goals,0.15,,,
AI capabilities and methodology,0.15,,,
Data and integration readiness,0.10,,,
Channel and domain expertise,0.08,,,
Measurement and experimentation,0.08,,,
Team quality and seniority,0.12,,,
Security and compliance,0.07,,,
Case studies and proof,0.08,,,
Cultural and process fit,0.05,,,
Price and value,0.12,,,
TOTAL,,=SUM(D2:D11),,

Scoring guidance:

• 5: Exemplary and validated with evidence and metrics
• 4: Strong with minor gaps
• 3: Adequate but requires support
• 2: Material weaknesses or unproven
• 1: Does not meet requirements

12 interview questions to separate signal from noise

1. Walk us through a recent engagement where you improved a core business metric. How did you measure lift and confidence?
2. What models or techniques do you use for our top use case, and how do you evaluate them?
3. Show the workflow from data ingestion to insight to activation. Who owns each step?
4. How do you prevent and detect hallucinations in generative outputs?
5. What are your standards for experiment design and stopping rules?
6. Describe your prompt libraries and content QA process, including brand and legal guardrails.
7. What happens if our data quality is lower than expected? What is your remediation plan?
8. How do you ensure reproducibility and documentation of your work?
9. What will be in our control versus yours, and what is the exit plan?
10. Describe a time your approach did not work. What changed after the postmortem?
11. Which third-party tools do you rely on, and how are they vetted for security and compliance?
12. How do you structure pilots so that success or failure is unambiguous?

Engagement and pricing models

Ask every bidder to map its price onto the structures below and to separate agency fees from pass-through costs such as AI tool licences, model usage and media.

Model	Typical structure	Watch for
Fixed-fee pilot	One fee for a defined scope, typically 6 to 12 weeks	Scope so narrow that a pass proves nothing
Monthly retainer	Flat fee for agreed deliverables; 3 to 6 month minimum terms are common	Vague deliverables, no rollover rules
Percentage of media	A share of ad spend, often around 10 to 20 percent, with a monthly minimum	Incentive to raise spend rather than efficiency
Base plus performance bonus	Lower base fee plus a bonus tied to an agreed KPI	KPIs that move without helping the business

These are typical structures, not quotes. Judge value by what each fee buys: named people, deliverables and the assets you keep.

Red flags when you hire an AI marketing agency

• No transparency: vague claims, no methodology, or unwillingness to explain models and measurement.
• Vanity metrics: focuses on clicks or impressions without tying to revenue, CAC, or LTV.
• Secret sauce dependency: proprietary black boxes with unclear IP or data rights that create lock-in.
• No human in the loop: fully automated content or decisioning with no quality control.
• Weak security posture: cannot provide a DPA, lacks SOC 2 or ISO controls, or ignores PII handling.
• Refuses a pilot: will not run a time-bound, fixed-fee proof of value with clear success criteria.
• Overpromising: guaranteed lifts or unrealistic timelines with no plan for data readiness.
• Poor references: cannot provide relevant references or shows case studies without baselines.
• One-trick tool: pitches a single model or platform for every problem regardless of fit.
• No experiment discipline: lacks holdouts, pre-post, or MMM where applicable.

Example 6-week selection timeline

1. Week 1: finalize objectives, data inventory, and RFP draft.
2. Week 2: issue RFP to shortlist, host one Q&A call, confirm demo use cases.
3. Week 3: receive proposals, run security questionnaires in parallel.
4. Week 4: vendor demos against the same brief, collect independent scores.
5. Week 5: team debrief, reference checks, negotiate pilot scope and price.
6. Week 6: finalize MSA, DPA, SOW, and kick off the pilot.

Pilot structure and SOW checkpoints

• Problem statement: the metric to move, baseline, and target lift.
• Data plan: sources, access, privacy controls, and transformation steps.
• Operating model: roles, meeting cadence, and decision rights.
• Experiment design: hypotheses, cohorts, timeline, and stopping rules.
• Success criteria: what constitutes a pass, including stats or confidence.
• Handover and exit: what is documented, what is yours to keep, and knowledge transfer.

What the first 90 days should look like

• Days 1 to 30: account and data access, a baseline for the pilot metric, an audit of current campaigns, and an approved test plan.
• Days 31 to 60: first experiments live, with weekly notes on what launched, what was learned and what changes next.
• Days 61 to 90: a readout against the SOW pass criteria, a scale, adjust or stop recommendation, and documented prompts, templates and dashboards you keep.

Judge the agency on the week-one metric against its baseline and a holdout where the channel allows. Output counts belong in weekly notes, not the scale decision.

Pro tip: Share the same data brief and demo script with every vendor. This makes comparisons fair and the final decision easier to defend.

To see how an integrated team answers this RFP, send it to us. We will reply with a pilot proposal, named team, pass criteria and pricing structure for our AI marketing services, ready to score on the same sheet as everyone else.

FAQ

What should an AI marketing agency RFP require?
Ask for workflow diagrams, governance policies, sample prompts, measurement plans, data handling practices, and references in your industry. Require proof of human QA, not just tool lists.

What are red flags when hiring an AI marketing agency?
Avoid vendors promising fully autonomous publishing, vague IP ownership, no audit trails, or inability to explain model selection and fallback processes.

How do you score AI agency proposals fairly?
Weight strategic fit, integration depth, compliance readiness, and pilot design over demo polish. Use the same scorecard for in-house build vs agency options when possible.

Who should own the prompts, templates and data an agency creates?
You should. Write ownership of prompt libraries, workflow configurations, creative files and dashboards into the contract, with a handover clause that applies whether the engagement scales or ends.